Quantifying Bias

Quantifying bias in artificial intelligence models, particularly large language models and text-to-image generators, is a crucial area of research aiming to identify and measure unfairness in their outputs and training data. Current efforts focus on developing both intrinsic and extrinsic bias metrics, often employing techniques like analyzing word embeddings, leveraging large language models for bias detection, and creating controlled datasets for benchmarking. These advancements are vital for improving the fairness and reliability of AI systems across various applications, ranging from legal decision-making to healthcare and marketing, ultimately promoting responsible AI development.